Garuda - Garba Rujukan Digital

EVALUASI START-UP TEAMPAL.ID BERBASIS WEBSITE DENGAN METODE RETROSPECTIVE THINK ALOUD (RTA) DAN POST STUDY SYSTEM USABILITY QUESTIONNAIRE (PSSUQ) A. A. Istri Ita Paramitha; I Made Artana; I Gede Irvan Pramanta Andika; Gede Deva Ranandra Putra; I Wayan Ady Purnawibawa; Anggun Setiyoningsih
Jurnal Teknologi Informasi dan Komputer Vol 6, No 3 (2020): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

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ABSTRACTThis study aims to determine the usability analysis results of the Teampal.id application in terms of users and find out interface improvement recommendations. Teampal.id is one of the start-ups under the guidance of the Primakara STMIK business incubator located in Denpasar-Bali. Start-up is a company that has just been established and is in the development and research phase to find the right market. Teampal.id is a start-up that includes a unique e-commerce that was formed in 2018. The product offered is a service for online farming communities. In this study using a combination of the Restrocpective Think Aloud (RTA) method and Post Study System Usability Questionnaire (PSSUQ). RTA is used to get results qualitatively while PSSUQ is to get quantitative results. This study used 30 respondents who came from active users of Teampal.id. Quantitative research results show that Teampal.id shows positive user acceptance results. This can be seen from the overall satisfaction score (OVERALL), which is 5.46 from a scale of 7. For system use (SYSUSE) scores 5.47, information quality (INFOQUAL) scores 5.47 and interface quality (INTERQUAL) scores 5.43. However, based on the results of research using the RTA method there are a number of things that must be corrected at the Teampal.id interface. Recommendations for improvements are made in high-fidelity wireframes, so they can be input in improving Teampal.id's interface. With this recommendation, it is expected to improve the quality of Teampal.id's user experience.Keywords : usability, RTA, PSSUABSTRAKPenelitian ini bertujuan untuk mengetahui hasil analisis kebergunaan (usability) dari aplikasi Teampal.id ditinjau dari sisi pengguna serta mengetahui rekomendasi perbaikan antarmuka. Teampal.id merupakan salah satu start-up binaan inkubator bisnis STMIK Primakara yang terletak di Denpasar-Bali. Start-up merupakan perusahaan yang baru saja didirikan dan berada dalam fase pengembangan dan penelitian untuk menemukan pasar yang tepat. Teampal.id adalah sebuah start-up yang termasuk unique e-commerce yang dibentuk pada tahun 2018. Produk yang ditawarkan adalah layanan untuk masyarakat berternak secara online. Dalam penelitian ini menggunakan metode kombinasi yaitu Restrocpective Think Aloud (RTA) dan Post Study System Usability Questionnaire (PSSUQ). RTA digunakan untuk mendapatkan hasil secara kualitatif sedangkan PSSUQ untuk mendapatkan hasil secara kuantitatif. Penelitian ini menggunakan 30 responden yang berasal dari pengguna aktif Teampal.id. Hasil penelitian secara kuantitatif bahwa Teampal.id menunjukkan hasil penerimaan pengguna yang positif. Hal ini dapat dilihat dari skor kepuasan secara keseluruhan (OVERALL) yaitu 5.46 dari skala 7. Untuk kegunaan sistem (SYSUSE)337 Jurnal Teknologi Informasi dan Komputer, Volume 6, Nomor 3, Oktober 2020memperoleh skor 5.47, kualitas informasi (INFOQUAL) memperoleh skor 5.47 dan kualitas antarmuka (INTERQUAL) memperoleh skor 5.43. Namun, berdasarkan hasil penelitian menggunakan metode RTA terdapat beberapa hal yang harus diperbaiki pada antarmuka Teampal.id. Rekomendasi perbaikan dibuat dalam high fidelity wireframe, sehingga dapat menjadi masukan dalam perbaikan antarmuka Teampal.id. Dengan rekomendasi ini diharapkan mampu meningkatkan kualitas pengalaman pengguna Teampal.id.Kata kunci: usability, RTA, PSSUQ

EVALUASI DAN ANALISIS USABILITY MENGGUNAKAN METODE USABILITY TESTING PADA APLIKASI PRE HOSPITAL STUDI KASUS NURSES APP I Gede Irvan Pramanta Andika; I Gede Juliana Eka Putra; Komang Tri Werthi
Jurnal Teknologi Informasi dan Komputer Vol 9, No 2 (2023): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

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ABSTRACTTraffic accidents are one of the biggest causes of death in the world. From WHO data, at least 1.35 million people die each year due to traffic accidents and 90% of them come from developing countries. This figure can actually be reduced by providing good pre-hospital services, such as easy accident reporting, recording the condition of victims and efficient communication between ambulances and hospitals. To make this happen, Startup G-One is here with one of its applications, namely the Nurses App, which helps record the condition of victims and communicates with referral hospitals. However, so that this application can be used easily by users, it is necessary to evaluate and analyze on the usability side. Therefore, the author made this study to measure the usability level of the Nurses App application with the Usability Testing Method so that suggestions for improvements can then be given in terms of the interface to increase the usability level of the application product. The results of the evaluation and analysis that have been carried out have succeeded in revealing all the problems in the Nurses G-One application where there are 10 problems and 11 recommendations for improvement which if improvements are made based on the recommendations given can streamline the time the user runs tasks using the application from 10.7 seconds to 10.7 seconds. 8.9 seconds and increases the smooth running of tasks, which initially only worked 69 tasks smoothly, then increased to 128 tasks.Keywords-Analysis, Evaluation, Nurses App, Usability Level, Usability TestingABSTRAKKecelakaan lalu lintas adalah salah satu penyebab kematian terbesar di dunia. Dari data WHO setidaknya ada 1,35 juta jiwa meninggal setiap tahunnya akibat kecelakaan lalu lintas dan 90% nya berasal dari negara berkembang. Angka ini sebenarnya bisa di tekan dengan menghadirkan layanan pre hospital yang baik, seperti pelaporan kecelakaan yang mudah, pencatatan kondisi korban serta komunikasi antara ambulan dan rumah sakit yang efisien. Untuk mewujudkan hal itu hadirlah Startup G-One dengan salah satu aplikasinya yaitu Nurses App, yang membantu pencatatan kondisi korban dan komunikasi dengan rumah sakit rujukan. Namun agar aplikasi ini dapat digunakan dengan mudah oleh pengguna, maka perlu dilakukan evaluasi dan analisis pada sisi usability. Oleh sebab itu penulis membuat penelitian ini untuk mengukur tingkat usability dari aplikasi Nurses App dengan Metode Usability Testing untuk kemudian dapat diberikan saran perbaikan dari segi interface untuk meningkatkan tingkat kegunaan dari produk aplikasi. Hasil dari evaluasi dan analisis yang telah dilakukan berhasil mengungkap semua permasalahan di aplikasi Nurses G-One yang mana terdapat 10 permasalah dan 11 rekomendasi perbaikan yang mana jika perbaikan dilakukan bedasarkan rekomendasi yang diberikan dapat mengefisienkan waktu user menjalankan tugas menggunakan aplikasi dari 10,7 detik menjadi 8,9 detik serta meningkatkan kelancaran pengerjaan task yang awalnya hanya 69 task yang lancar dikerjakan kemudian bertambah menjadi 128 task.Kata kunci-Analisis, Evaluasi, Nurses App, Tingkat Kegunaan, Usability Testing

YOLOv9-Based Object Detection Model For Pig Feces On Pig SKIN: Improving Biosecurity In Automated Cleaning Systems Andika, I Gede Irvan Pramanta; Sudarma, I Made; Swamardika, Ida Bagus Alit; Sedana, I Made Bagus Ambara
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4240

This study developed an object detection model using YOLOv9 to identify pig feces on pig skin, addressing challenges in automating pig cleaning systems and reducing the spread of African Swine Fever (ASF). The aim was to enhance biosecurity measures by minimizing human-pig contact through automation. A specialized dataset comprising 5,404 images was collected from Nyoman Farm in Bali, Indonesia, under various lighting and cleanliness conditions. These images were annotated into two classes, namely 'feces' and 'pig,' following strict criteria to ensure clarity and distinction. YOLOv9 was chosen as it is an advanced update of YOLOv8 with enhanced object detection capabilities. The model was iteratively trained and optimized to achieve the best performance. The results achieved a mAP_0.5 of 70.5%, precision of 70.6%, and recall of 72.1%. However, the model faced challenges in distinguishing pig skin patterns from feces and managing false positives caused by similar-looking objects in the barn environment. Despite these challenges, integrating this model into an automated cleaning system can reduce human-pig contact by up to 76%, which is expected to significantly lower the risk of ASF transmission. This study contributes to automated farming technology, demonstrating how well YOLOv9 can detect complex objects in agricultural settings and providing practical solutions to enhance biosecurity in pig farming while improving productivity.

NATURAL LANGUAGE PROCESSING UNTUK EKSTRAKSI INFORMASI ADVERSE DRUG REACTIONS DARI ELECTRONIC HEALTH RECORDS: SYSTEMATIC REVIEW I Gede, Irvan Pramanta Andika; Wiradarma, Riska; May Arfian, Dody
Journal Pharmactive Vol. 5 No. 1 (2026): Jurnal Pharmactive April
Publisher : Institut Teknologi dan Kesehatan Bintang Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64036/pharmactive.v5i1.100

Adverse drug events (ADEs) contribute to 5-10% of hospitalizations and cost approximately USD 30 billion annually, yet spontaneous reporting systems capture only 5-10% of actual ADEs due to severe underreporting. This systematic review analyzed 60 peer-reviewed studies (2019-2025) on natural language processing (NLP) methods for extracting ADE information from electronic health record (EHR) clinical notes, following PRISMA 2020 guidelines across five databases (PubMed, IEEE Xplore, ACL Anthology, Scopus, Web of Science). Results demonstrate that transformer-based models, particularly BioBERT and ClinicalBERT, represent the state-of-the-art with F1-scores of 0.85-0.92 on benchmark datasets (n2c2 2018, MIMIC-III, MADE1.0), significantly outperforming rule-based systems (+15-20%) and traditional machine learning methods (+8-12%). Domain-specific pre-training on clinical text proved crucial, improving performance by 3-5% over general BERT models. However, critical challenges persist: negation and speculation detection (30-40% of medical mentions require contextual disambiguation), temporal reasoning for determining ADE onset relative to drug exposure, ambiguous medical abbreviation resolution, and causality assessment. A significant lab-to-clinic gap of 10-15% performance degradation was identified, with only 8% of studies reporting actual clinical deployment experiences. Reproducibility remains problematic, with merely 23% of studies sharing code and 15% providing trained models. Future priorities include developing few-shot learning approaches to address limited labeled data (~5,000 annotated clinical notes publicly available), enhancing model interpretability through explainable AI methods, conducting multi-center external validation studies, and establishing standardized evaluation protocols. This review provides evidence-based guidance for researchers developing NLP methods, practitioners implementing ADE detection systems, and policymakers formulating standards for NLP-based pharmacovigilance.

APLIKASI MACHINE LEARNING DALAM PREDIKSI INTERAKSI OBAT: SYSTEMATIC REVIEW May Arfian, Dody; I Gede, Irvan Pramanta Andika; Wiradarma, Riska
Journal Pharmactive Vol. 5 No. 1 (2026): Jurnal Pharmactive April
Publisher : Institut Teknologi dan Kesehatan Bintang Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64036/pharmactive.v5i1.104

Drug-drug interactions (DDIs) represent a significant challenge in modern pharmacotherapy, contributing to 17-23% of adverse drug reaction-related hospitalizations. Machine learning (ML) has emerged as a promising approach for computational DDI prediction, yet a comprehensive synthesis of methodologies, performance benchmarks, and clinical translation challenges remains lacking. This systematic review aims to identify and evaluate ML algorithms applied to DDI prediction, compare their performance across different datasets and validation strategies, analyze feature representation methods, and identify critical gaps impeding clinical deployment. Following PRISMA 2020 guidelines, we conducted a systematic search across five electronic databases (PubMed, IEEE Xplore, Scopus, Web of Science, Google Scholar) for studies published between January 2019 and March 2025. Dual independent screening and extraction were performed with quality assessment using adapted PROBAST criteria. Included studies were analyzed for algorithm types, feature representations, datasets, validation strategies, and performance metrics. From 1,285 initial records, 60 high-quality studies were included. Graph neural networks (GNNs) emerged as state-of-the-art methods (mean F1-score: 0.931 ± 0.024), significantly outperforming traditional ML (0.842 ± 0.038, p < 0.001) and deep neural networks (0.893 ± 0.031, p = 0.003). Multi-modal approaches integrating chemical structure, biological targets, and phenotypic data achieved highest performance (F1: 0.945-0.982). DrugBank was the most utilized dataset (63.3% of studies), though severe class imbalance (positive:negative ratio 1:20 to 1:50) posed significant challenges. Critical gaps identified include: cold-start problem (18.3% performance degradation for unseen drugs), interpretability issues (45% black-box models), and minimal real-world validation (only 6.7% used EHR data). A severe reproducibility crisis was evident, with only 11.7% of studies fully reproducible. While ML-based DDI prediction has achieved impressive benchmark performance, substantial challenges remain for clinical translation. Priority research directions include: developing explainable AI methods for biological validation, addressing cold-start generalization through meta-learning and transfer learning, conducting multi-center real-world validation studies, establishing standardized evaluation protocols, and implementing federated learning infrastructure for privacy-preserving collaboration. Community-wide efforts toward reproducibility, standardization, and responsible deployment are essential for translating computational advances into clinically impactful systems that enhance medication safety.

PERANCANGAN SISTEM INFOPRMASI APOTEK PHARFACILLE BERBASIS WABSITE DENGAN TIGA TAHAP METODE AGAILE I Kadek , Krisna Angga Pamungkas; Moch , Anwar Fery Rais; I Gede, Irvan Pramanta Andika
Journal Pharmactive Vol. 5 No. 1 (2026): Jurnal Pharmactive April
Publisher : Institut Teknologi dan Kesehatan Bintang Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64036/pharmactive.v5i1.105

The development of information technology has driven digital transformation across various sectors, including pharmaceutical services in pharmacies. Common problems faced by pharmacies that still rely on manual systems include inaccurate stock recording, delays in report generation, and low service efficiency. This study aims to design and develop a web-based pharmacy information system named PHARFACILLE to improve the effectiveness and accuracy of pharmacy operational management. The method used in this study is the Software Development Life Cycle (SDLC) combined with the Agile approach using the Scrum framework. System development was carried out iteratively through several sprints, covering planning, analysis, design, implementation, and evaluation stages. System modeling was conducted using Unified Modeling Language (UML) to describe system requirements and process flows. The results show that the PHARFACILLE system was successfully developed with main features including user authentication, sales dashboard, cashier system, inventory management, ordering and goods receiving management, and financial reporting. Integration between modules enables real-time data updates, thereby improving stock accuracy and transaction efficiency. Based on the development results, this system has the potential to enhance pharmacy service quality, accelerate operational processes, and support managerial decision-making through more accurate and structured data presentation.

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